# Sampling clustering based on multi-view attribute structural relations

## TL;DR

This paper introduces a new method for clustering multi-view graph data that avoids deep learning and handles complex relationships more effectively.

## Contribution

The novel SLMGC approach improves multi-view graph clustering by using graph filtering, sampling, and contrastive regularization without deep neural networks.

## Key findings

- SLMGC outperforms deep learning methods in multi-view graph clustering tasks.
- The method effectively handles varying features and relationships in multi-view data.
- It reduces computational complexity by focusing on node importance for sampling.

## Abstract

In light of the exponential growth in information volume, the significance of graph data has intensified. Graph clustering plays a pivotal role in graph data processing by jointly modeling the graph structure and node attributes. Notably, the practical significance of multi-view graph clustering is heightened due to the presence of diverse relationships within real-world graph data. Nonetheless, prevailing graph clustering techniques, predominantly grounded in deep learning neural networks, face challenges in effectively handling multi-view graph data. These challenges include the incapability to concurrently explore the relationships between multiple view structures and node attributes, as well as difficulties in processing multi-view graph data with varying features. To tackle these issues, this research proposes a straightforward yet effective multi-view graph clustering approach known as SLMGC. This approach uses graph filtering to filter noise, reduces computational complexity by extracting samples based on node importance, enhances clustering representations through graph contrastive regularization, and achieves the final clustering outcomes using a self-training clustering algorithm. Notably, unlike neural network algorithms, this approach avoids the need for intricate parameter settings. Comprehensive experiments validate the supremacy of the SLMGC approach in multi-view graph clustering endeavors when contrasted with prevailing deep neural network techniques.